MACHINE LEARNING-BASED OPTIMIZATION OF AIRFARE PRICE FORECASTING

Authors

  • Kammari Supriya Student Department of CSE St Johns College Of Engineering And Technology, Yemmiganur, Kurnool, AP Author
  • Gandla Nagappa Associate professor Department of CSE St Johns College Of Engineering And Technology, Yemmiganur, Kurnool, AP Author

DOI:

https://doi.org/10.48047/wwg0qk10

Keywords:

.

Abstract

Accurate airfare price forecasting is essential for both travelers and airline companies, enabling cost-effective bookings and optimized revenue management. Airfare prices are highly dynamic, influenced by factors such as seasonality, demand fluctuations, airline competition, and fuel costs. Conventional price prediction methods struggle to adapt to these complex variations, often leading to inaccurate forecasts.

Downloads

Download data is not yet available.

References

Concept of a Numerical Forecast Model. Accessed: Aug. 10, 2023. [Online]. Available: http://web.kma.go.kr/aboutkma/intro/superc

om/model/model_concept.jsp

P. Davis, C. Ruth, A. A. Scaife, and J. Kettleborough, ‘‘A large ensemble seasonal forecasting system: GloSea6,’’ Dec. 2020,

vol. 2020.

Downloads

Published

2025-03-10

Issue

Vol. 54 No. 5 (2025): Year 2025 ,Volume 54 Issue 5

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

Supriya, K., & Nagappa, G. . (2025). MACHINE LEARNING-BASED OPTIMIZATION OF AIRFARE PRICE FORECASTING. Cuestiones De Fisioterapia, 54(5), 1-9. https://doi.org/10.48047/wwg0qk10